- Bootstrap methods assume that our sample is an SRS and will give trustworthy conclusions only if this condition is met.
-
These methods make no assumptions about the distribution your data comes from.
- For inference for a single population mean, single-parameter bootstrap tests are an alternative to the one-sample $t$-confidence interval when the guidelines for its use are not met (such as when the data is strongly skewed or has outliers with low sample size).
- For inference for a single population proportion, single-parameter bootstrap tests are an alternative to the one-sample $z$-confidence interval when the guidelines for its use are not met, that is when the number of successes and failures are not large enough.
| Data | Frequencies | |
| Sample data goes here (enter numbers in columns): | ||
| Number of Successes | Sample Size | |
| Calculate Interval for a: | ||
| Level of Confidence: | ||
| Number of Bootstrap Samples: | ||
| Input data as Frequency Table: |
Population Mean
| Sample Size: | $n=$ |
| Sample Mean: | $\overline{x}=$ |
| Confidence Interval for the True Mean: | |
| Frequency | ||
| Sample Data | Bootstrap Means $\mu^*$ |
Population Proportion
| Sample Size: | $n=$ |
| Sample Proportion: | $\hat{p}=$ |
| Confidence Interval for the True Proportion: | |
| Frequency | ||
| Failures and Successes | Bootstrap Proportions $p^*$ |
Population Median
| Sample Size: | $n=$ |
| Sample Median: | $M=$ |
| Confidence Interval for the True Median: | |
| Frequency | ||
| Sample Data | Bootstrap Medians $M^*$ |
Population Standard Deviation
| Sample Size: | $n=$ |
| Sample Standard Deviation: | $s=$ |
| Confidence Interval for the True Standard Deviation: | |
| Frequency | ||
| Sample Data | Bootstrap Standard Deviations $\sigma^*$ |